克隆选择算法-python实现

CSAIndividual.py

 import numpy as np
 import ObjFunction

 class CSAIndividual:

     '''
     individual of clone selection algorithm
     '''

     def __init__(self,  vardim, bound):
         '''
         vardim: dimension of variables
         bound: boundaries of variables
         '''
         self.vardim = vardim
         self.bound = bound
         self.fitness = 0.
         self.trials = 0

     def generate(self):
         '''
         generate a random chromsome for clone selection algorithm
         '''
         len = self.vardim
         rnd = np.random.random(size=len)
         self.chrom = np.zeros(len)
         for i in xrange(0, len):
             self.chrom[i] = self.bound[0, i] + \
                 (self.bound[1, i] - self.bound[0, i]) * rnd[i]

     def calculateFitness(self):
         '''
         calculate the fitness of the chromsome
         '''
         self.fitness = ObjFunction.GrieFunc(
             self.vardim, self.chrom, self.bound)

CSA.py

 import numpy as np
 from CSAIndividual import CSAIndividual
 import random
 import copy
 import matplotlib.pyplot as plt

 class CloneSelectionAlgorithm:

     '''
     the class for clone selection algorithm
     '''

     def __init__(self, sizepop, vardim, bound, MAXGEN, params):
         '''
         sizepop: population sizepop
         vardim: dimension of variables
         bound: boundaries of variables
         MAXGEN: termination condition
         params: algorithm required parameters, it is a list which is consisting of[beta, pm, alpha_max, alpha_min]
         '''
         self.sizepop = sizepop
         self.vardim = vardim
         self.bound = bound
         self.MAXGEN = MAXGEN
         self.params = params
         self.population = []
         self.fitness = np.zeros(self.sizepop)
         self.trace = np.zeros((self.MAXGEN, 2))

     def initialize(self):
         '''
         initialize the population of ba
         '''
         for i in xrange(0, self.sizepop):
             ind = CSAIndividual(self.vardim, self.bound)
             ind.generate()
             self.population.append(ind)

     def evaluation(self):
         '''
         evaluation the fitness of the population
         '''
         for i in xrange(0, self.sizepop):
             self.population[i].calculateFitness()
             self.fitness[i] = self.population[i].fitness

     def solve(self):
         '''
         the evolution process of the clone selection algorithm
         '''
         self.t = 0
         self.initialize()
         self.evaluation()
         bestIndex = np.argmax(self.fitness)
         self.best = copy.deepcopy(self.population[bestIndex])
         while self.t < self.MAXGEN:
             self.t += 1
             tmpPop = self.reproduction()
             tmpPop = self.mutation(tmpPop)
             self.selection(tmpPop)
             best = np.max(self.fitness)
             bestIndex = np.argmax(self.fitness)
             if best > self.best.fitness:
                 self.best = copy.deepcopy(self.population[bestIndex])

             self.avefitness = np.mean(self.fitness)
             self.trace[self.t - 1, 0] = \
                 (1 - self.best.fitness) / self.best.fitness
             self.trace[self.t - 1, 1] = (1 - self.avefitness) / self.avefitness
             print("Generation %d: optimal function value is: %f; average function value is %f" % (
                 self.t, self.trace[self.t - 1, 0], self.trace[self.t - 1, 1]))
         print("Optimal function value is: %f; " % self.trace[self.t - 1, 0])
         print "Optimal solution is:"
         print self.best.chrom
         self.printResult()

     def reproduction(self):
         '''
         reproduction
         '''
         tmpPop = []
         for i in xrange(0, self.sizepop):
             nc = int(self.params[1] * self.sizepop)
             for j in xrange(0, nc):
                 ind = copy.deepcopy(self.population[i])
                 tmpPop.append(ind)
         return tmpPop

     def mutation(self, tmpPop):
         '''
         hypermutation
         '''
         for i in xrange(0, self.sizepop):
             nc = int(self.params[1] * self.sizepop)
             for j in xrange(1, nc):
                 rnd = np.random.random(1)
                 if rnd < self.params[0]:
                     # alpha = self.params[
                     #     2] + self.t * (self.params[3] - self.params[2]) / self.MAXGEN
                     delta = self.params[2] + self.t * \
                         (self.params[3] - self.params[3]) / self.MAXGEN
                     tmpPop[i * nc + j].chrom += np.random.normal(0.0, delta, self.vardim)
                     # tmpPop[i * nc + j].chrom += alpha * np.random.random(
                     # self.vardim) * (self.best.chrom - tmpPop[i * nc +
                     # j].chrom)
                     for k in xrange(0, self.vardim):
                         if tmpPop[i * nc + j].chrom[k] < self.bound[0, k]:
                             tmpPop[i * nc + j].chrom[k] = self.bound[0, k]
                         if tmpPop[i * nc + j].chrom[k] > self.bound[1, k]:
                             tmpPop[i * nc + j].chrom[k] = self.bound[1, k]
                     tmpPop[i * nc + j].calculateFitness()
         return tmpPop

     def selection(self, tmpPop):
         '''
         re-selection
         '''
         for i in xrange(0, self.sizepop):
             nc = int(self.params[1] * self.sizepop)
             best = 0.0
             bestIndex = -1
             for j in xrange(0, nc):
                 if tmpPop[i * nc + j].fitness > best:
                     best = tmpPop[i * nc + j].fitness
                     bestIndex = i * nc + j
             if self.fitness[i] < best:
                 self.population[i] = copy.deepcopy(tmpPop[bestIndex])
                 self.fitness[i] = best

     def printResult(self):
         '''
         plot the result of clone selection algorithm
         '''
         x = np.arange(0, self.MAXGEN)
         y1 = self.trace[:, 0]
         y2 = self.trace[:, 1]
         plt.plot(x, y1, 'r', label='optimal value')
         plt.plot(x, y2, 'g', label='average value')
         plt.xlabel("Iteration")
         plt.ylabel("function value")
         plt.title("Clone selection algorithm for function optimization")
         plt.legend()
         plt.show()

运行程序：

 if __name__ == "__main__":

     bound = np.tile([[-600], [600]], 25)
     csa = CSA(50, 25, bound, 500, [0.3, 0.4, 5, 0.1])
     csa.solve()

ObjFunction见简单遗传算法-python实现。